Fix copyright

Run the content filter concurrently with LLM text generation
using ParallelPipeline, with a ContentFilterGate that blocks
output until the filter approves or rejects the content.

examples/foundational/57-parallel-content-filter.py

@@ -0,0 +1,254 @@
+#
+# Copyright (c) 2024–2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import os
+from dataclasses import dataclass
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
+from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import (
+    ControlFrame,
+    Frame,
+    LLMContextFrame,
+    LLMFullResponseEndFrame,
+    LLMRunFrame,
+    SystemFrame,
+    TTSSpeakFrame,
+)
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+
+@dataclass
+class ContentApprovedFrame(ControlFrame):
+    """Signal frame indicating content passed the filter."""
+
+    pass
+
+
+@dataclass
+class ContentRejectedFrame(ControlFrame):
+    """Signal frame indicating content was rejected by the filter."""
+
+    pass
+
+
+FILTERED_WORDS = ["apple", "banana", "car"]
+
+
+class ContentFilterProcessor(FrameProcessor):
+    """Checks LLMContextFrames for filtered words and emits signal frames.
+
+    Runs in one branch of a ParallelPipeline. Emits ContentApprovedFrame or
+    ContentRejectedFrame so that a downstream ContentFilterGate can decide
+    whether to let the LLM's output through.
+    """
+
+    def _contains_filtered_words(self, context: "LLMContext") -> bool:
+        """Check if the last message in the context contains any filtered words."""
+        messages = context.messages
+        if messages:
+            last_message = messages[-1]
+            content = last_message.get("content", "") if isinstance(last_message, dict) else ""
+            if isinstance(content, str):
+                content_lower = content.lower()
+                if any(word in content_lower for word in FILTERED_WORDS):
+                    logger.info(f"Filtered content detected: {content}")
+                    return True
+        return False
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, LLMContextFrame):
+            if self._contains_filtered_words(frame.context):
+                await self.push_frame(ContentRejectedFrame(), direction)
+            else:
+                await self.push_frame(ContentApprovedFrame(), direction)
+            return
+
+        await self.push_frame(frame, direction)
+
+
+class ContentFilterGate(FrameProcessor):
+    """Gates LLM output until the content filter signals approval or rejection.
+
+    Placed after a ParallelPipeline that runs a ContentFilterProcessor alongside
+    an LLM. Because the content filter (a fast regex check) completes before the
+    LLM's first token arrives, the signal frame always reaches this gate first.
+
+    - On ContentApprovedFrame: subsequent LLM output passes through normally.
+    - On ContentRejectedFrame: LLM output is discarded and a canned rejection
+      message is spoken instead via TTSSpeakFrame.
+
+    Note: For a production implementation with a slow content filter (e.g. an
+    external moderation API), you would add frame buffering so that LLM output
+    arriving before the filter decision is held rather than passed through.
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._rejecting = False
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        await super().process_frame(frame, direction)
+
+        # System frames always pass through.
+        if isinstance(frame, SystemFrame):
+            await self.push_frame(frame, direction)
+            return
+
+        # Content filter approved — LLM output will pass through normally.
+        if isinstance(frame, ContentApprovedFrame):
+            return
+
+        # Content filter rejected — suppress LLM output and speak a rejection.
+        if isinstance(frame, ContentRejectedFrame):
+            self._rejecting = True
+            await self.push_frame(
+                TTSSpeakFrame(text="I'm sorry, I can't respond to that."), direction
+            )
+            return
+
+        # LLMFullResponseEndFrame marks the end of the LLM's response.
+        # When rejecting, consume it to finish suppression.
+        if isinstance(frame, LLMFullResponseEndFrame) and self._rejecting:
+            self._rejecting = False
+            return
+
+        # While rejecting, discard all other frames (LLM text, etc.).
+        if self._rejecting:
+            return
+
+        await self.push_frame(frame, direction)
+
+
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
+        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY", ""))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY", ""),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
+        },
+    ]
+
+    context = LLMContext(messages)
+    context_aggregator = LLMContextAggregatorPair(context)
+    content_filter = ContentFilterProcessor()
+    content_gate = ContentFilterGate()
+
+    # The content filter and LLM run in parallel. The content filter emits
+    # a signal frame (approved/rejected) while the LLM generates text
+    # concurrently. The gate after the ParallelPipeline blocks output until
+    # the content filter decides. TTS is placed after the gate so rejected
+    # content never reaches it.
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,
+            context_aggregator.user(),  # User responses
+            ParallelPipeline(
+                [content_filter],  # Branch 1: content filter (emits signal frames)
+                [llm],  # Branch 2: LLM text generation
+            ),
+            content_gate,  # Gates output until content filter approves
+            tts,  # TTS (only processes approved text)
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()